• Search by Module
• Search by Words
• Search Projects

• Java
• Python
• JavaScript
• TypeScript
• C++
• Scala
• Blog

Project: Deep-Learning-TensorFlow (GitHub Link)

• Deep-Learning-TensorFlow-master
  • cmd_line
    • recurrent
      • run_lstm.py
      • __init__.py
    • autoencoders
      • run_stacked_autoencoder_unsupervised.py
      • run_stacked_autoencoder_supervised.py
      • __init__.py
      • run_autoencoder.py
    • linear
      • run_logistic_regression.py
      • __init__.py
    • __init__.py
    • convolutional
      • run_conv_net.py
      • __init__.py
    • boltzmann
      • run_deep_autoencoder.py
      • __init__.py
      • run_rbm.py
      • run_dbn.py
  • setup.py
  • yadlt
    • utils
      • utilities.py
      • tf_utils.py
      • datasets.py
      • __init__.py
    • models
      • recurrent
        • lstm.py
        • __init__.py
      • autoencoders
        • denoising_autoencoder.py
        • stacked_denoising_autoencoder.py
        • deep_autoencoder.py
        • __init__.py
      • linear
        • __init__.py
        • logistic_regression.py
      • __init__.py
      • convolutional
        • conv_net.py
        • __init__.py
      • boltzmann
        • dbn.py
        • rbm.py
        • deep_autoencoder.py
        • __init__.py
    • __init__.py
    • core
      • unsupervised_model.py
      • supervised_model.py
      • config.py
      • trainers.py
      • model.py
      • layers.py
      • __init__.py
  • README.md
  • tests
    • test_utils.py
    • __init__.py
  • requirements.txt
  • index.rst
  • .gitignore
  • LICENSE.txt

Deep Learning algorithms with TensorFlow

This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library. This package is intended as a command line utility you can use to quickly train and evaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets. If you want to use the package from ipython or maybe integrate it in your code, I published a pip package named yadlt: Yet Another Deep Learning Tool.

Requirements:

• tensorflow >= 1.0

List of available models:

• Convolutional Network
• Restricted Boltzmann Machine
• Deep Belief Network
• Deep Autoencoder as stack of RBMs
• Denoising Autoencoder
• Stacked Denoising Autoencoder
• Deep Autoencoder as stack of Denoising Autoencoders
• MultiLayer Perceptron
• Logistic Regression

Installation

Through pip:

pip install yadlt

You can learn the basic usage of the models by looking at the command_line/ directory. Or you can take a look at the documentation.

Note: the documentation is still a work in progress for the pip package, but the package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object (e.g. sdae = StackedDenoisingAutoencoder()) and call the fit/predict methods, and the pretrain() method if the model supports it (e.g. sdae.pretrain(X_train, y_train), sdae.fit(X_train, y_train) and predictions = sdae.predict(X_test))

Through github:

• cd in a directory where you want to store the project, e.g. /home/me
• clone the repository: git clone https://github.com/blackecho/Deep-Learning-TensorFlow.git
• cd Deep-Learning-TensorFlow
• now you can configure the software and run the models (see the documentation)!

Documentation:

You can find the documentation for this project at this link.

Models TODO list

• Recurrent Networks (LSTMs)
• Variational Autoencoders
• Deep Q Reinforcement Learning